The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

Trauma is an important cause of death in young- and middle-aged people. Trauma is comprehensive and includes many surgical specialties, and the surgical techniques of these specialties have long been mature. To reduce the mortality and disability rate of trauma patients, it is necessary to improve trauma management. Trauma has attracted attention in China and trauma treatment and care developed rapidly in recent years. To decrease traumatic mortality and disability rates, our team is committed to building an efficient trauma system in Shaanxi province and has successfully developed a trauma limb salvage map to address the high rates of amputation and disability in patients with limb injuries. This article elaborates on the construction experience of a trauma limb salvage map and its application details in Shaanxi province of China.
Humans ; China ; Limb Salvage/methods* ; Wounds and Injuries/surgery* ; Male ; Extremities/injuries* ; Adult ; Amputation, Surgical ; Middle Aged ; Female

Sigma-1 receptor (σ ₁R) has become a focus point of drug discovery for central nervous system (CNS) diseases. A series of novel 1-phenylethan-1-one O-(2-aminoethyl) oxime derivatives were synthesized. In vitro biological evaluation led to the identification of 1a, 14a, 15d and 16d as the most high-affinity (K _i < 4 nmol/L) and selective σ ₁R agonists. Among these, 15d, the most metabolically stable derivative exhibited high selectivity for σ ₁R in relation to σ ₂R and 52 other human targets. In addition to low CYP450 inhibition and induction, 15d also exhibited high brain permeability and excellent oral bioavailability. Importantly, 15d demonstrated effective antipsychotic potency, particularly for alleviating negative symptoms and improving cognitive impairment in experimental animal models, both of which are major challenges for schizophrenia treatment. Moreover, 15d produced no significant extrapyramidal symptoms, exhibiting superior pharmacological profiles in relation to current antipsychotic drugs. Mechanistically, 15d inhibited GSK3β and enhanced prefrontal BDNF expression and excitatory synaptic transmission in pyramidal neurons. Collectively, these in vivo proof-of-concept findings provide substantial experimental evidence to demonstrate that modulating σ ₁R represents a potential new therapeutic approach for schizophrenia. The novel chemical entity along with its favorable drug-like and pharmacological profile of 15d renders it a promising candidate for treating schizophrenia.

Here, we report a previously unrecognized syndromic neurodevelopmental disorder associated with biallelic loss-of-function variants in the RBM42 gene. The patient is a 2-year-old female with severe central nervous system (CNS) abnormalities, hypotonia, hearing loss, congenital heart defects, and dysmorphic facial features. Familial whole-exome sequencing (WES) reveals that the patient has two compound heterozygous variants, c.304C>T (p.R102*) and c.1312G>A (p.A438T), in the RBM42 gene which encodes an integral component of splicing complex in the RNA-binding motif protein family. The p.A438T variant is in the RRM domain which impairs RBM42 protein stability in vivo. Additionally, p.A438T disrupts the interaction of RBM42 with hnRNP K, which is the causative gene for Au-Kline syndrome with overlapping disease characteristics seen in the index patient. The human R102* or A438T mutant protein failed to fully rescue the growth defects of RBM42 ortholog knockout ΔFgRbp1 in Fusarium while it was rescued by the wild-type (WT) human RBM42. A mouse model carrying Rbm42 compound heterozygous variants, c.280C>T (p.Q94*) and c.1306_1308delinsACA (p.A436T), demonstrated gross fetal developmental defects and most of the double mutant animals died by E13.5. RNA-seq data confirmed that Rbm42 was involved in neurological and myocardial functions with an essential role in alternative splicing (AS). Overall, we present clinical, genetic, and functional data to demonstrate that defects in RBM42 constitute the underlying etiology of a new neurodevelopmental disease which links the dysregulation of global AS to abnormal embryonic development.
Female ; Animals ; Mice ; Humans ; Child, Preschool ; Intellectual Disability/genetics* ; Heart Defects, Congenital/genetics* ; Facies ; Cleft Palate ; Muscle Hypotonia

Objective To introduce a partially nested design based on the characteristics of TCM in treating the same disease with different treatments and syndrome differentiation and treatment.Methods Partially nested design was used for standardized treatment of complex interventions.The TCM group was divided into multiple subsets according to"syndrome type-treatment method-prescription"(with nested structure),while the control group was treated with standardized Western medicine(without nested structure);taking a case study of"different treatments for the same disease"data for ulcerative colitis,this design type was applied and analyzed using a multi-level model.Results The partially nested design was consistent with the feature of TCM of"different treatments for the same disease"and met the methodological requirements for evidence-based evaluation.Multilevel models allowed analyses with this type of data.Conclusion The use of partially nested design enables the evaluation of the comprehensive effectiveness of"different treatments for the same disease",which can provide a methodological reference for the assessment of clinical effectiveness of TCM.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective To describe the current state of research and future research hotspots through a metrological analysis of the literature in the field of forensic anthropological remains identification re-search.Methods The data retrieved and extracted from the Web of Science Core Collection (WoSCC),the core database of the Web of Science information service platform (hereinafter referred to as "WoS"),was used to analyze the trends and topic changes in research on forensic identification of human re-mains from 1991 to 2022.Network visualisation of publication trends,countries (regions),institutions,authors and topics related to the identification of remains in forensic anthropology was analysed using python 3.9.2 and Gephi 0.10.Results A total of 873 papers written in English in the field of forensic anthropological remains identification research were obtained.The journal with the largest number of publications was Forensic Science International (164 articles).The country (region) with the largest number of published papers was China (90 articles).Katholieke Univ Leuven (Netherlands,21 articles) was the institution with the largest number of publications.Topic analysis revealed that the focus of forensic anthropological remains identification research was sex estimation and age estimation,and the most commonly studied remains were teeth.Conclusion The volume of publications in the field of forensic anthropological remains identification research has a distinct phasing.However,the scope of both international and domestic collaborations remains limited.Traditionally,human remains identifica-tion has primarily relied on key areas such as the pelvis,skull,and teeth.Looking ahead,future re-search will likely focus on the more accurate and efficient identification of multiple skeletal remains through the use of machine learning and deep learning techniques.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.